feat(search): pluggable vector/embedding search with hybrid FTS5+RRF

[jtomaszon]

Summary

Adds semantic search capability alongside existing FTS5 keyword search, following the architecture endorsed in #21 and #24.

• New internal/embedding/ package — Provider interface with Ollama and OpenAI implementations. Pure-Go cosine similarity, binary serialization, and Reciprocal Rank Fusion (RRF) merge. No new dependencies beyond net/http.
• Hybrid search — When an embedding provider is configured, Search() runs FTS5 + vector cosine similarity and merges results via RRF (k=60). Falls back to FTS5-only when no provider is set — zero overhead for existing users.
• Async embedding on save/update — AddObservation and UpdateObservation trigger background embedding generation. Content changes re-embed automatically.
• observation_embeddings table — Created on migration, nullable. Separate from observations to keep FTS5 scans fast.
• CLI flags — --embedding-provider=ollama|openai|none, --embedding-model, --embedding-url, plus ENGRAM_EMBEDDING_* env vars.
• engram backfill-embeddings — Bulk-embeds existing observations that don't have embeddings yet.

Context

This implements the approach discussed by @Gentleman-Programming in #21 and #24:

1. FTS5 stays default — zero overhead when no provider configured
2. Vector search as opt-in via pluggable providers
3. Hybrid search merging FTS5 + vector results

Design decisions

Decision	Rationale
FTS5 stays default	Existing behavior unchanged when no provider configured
Brute-force cosine	Personal memory is typically <10K obs. Benchmark: 564ns/op on 768-dim (56ms for 100K obs)
RRF over weighted linear	Rank-based, no need to normalize BM25 and cosine to same scale
Separate embeddings table	Large BLOBs (~3KB each) would bloat observation table scans
Async embedding	Network calls (50-500ms) shouldn't block observation saves
No new Go deps	Providers use stdlib net/http only. CGO_ENABLED=0 preserved
Agent-agnostic providers	Users plug Ollama (local/free) or OpenAI (cloud) via config

Stats

• 1,495 lines added across 9 files (6 new + 3 modified)
• 33 new tests (22 embedding + 11 store integration)
• All 346 existing tests pass — zero regressions
• Benchmark: BenchmarkCosineSimilarity768: 564.5 ns/op, 0 allocs

Refs #21, #24

Test plan

• All existing 346 tests pass unchanged
• New embedding provider tests with mock HTTP servers
• Cosine similarity correctness (identical=1, orthogonal=0, opposite=-1)
• RRF merge ordering verified with known inputs
• Store integration: hybrid search returns results from both FTS5 and vector
• Store integration: vector search respects project/scope/type filters
• Backfill generates embeddings for all unembedded observations
• No-provider path returns FTS5-only results (backward compatible)
• Embedding table created on migration

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